Description Usage Arguments Details Value Author(s) References See Also Examples
Temporal block bootstrap for data at spatial locations (holding locations constant at each iteration). This is a wrapper function to the tsboot or boot functions for use with the field significance approach of Elmore et al. (2006).
1 2 3 4 5 
Z 
n by m numeric matrix whose rows represent contiguous time points, and whose columns represent spatial locations. 
numrep 
numeric/integer giving the number of bootstrap replications to use. 
block.length 
positive numeric/integer giving the desired block lengths. If NULL, 
bootfun 
character naming an R function to be applied to each replicate sample. Must return a single number, but is otherwise the 
alpha 
numeric giving the value of 
bca 
logical, should biascorrected and adjusted (BCa) CI's be calculated? Only used if 
x 
data frame of class “LocSig” as returned by 
loc 
m by 2 matrix of location coordinates. 
nx,ny 
If 
... 

This function performs the circular block bootstrap algorithm over time at each of m locations (columns of x
). So, at each bootstrap iteration, entire blocks of rows of x are resampled with replacement. If Z
represents forecast errors at grid points, and bootfun
=“mean”, then this finds the gridpoint CI's in steps 1 (a) to 1 (c) of Elmore et al. (2006).
LocSig: A data frame with class attribute “LocSig” with components:
Estimate 
numeric giving the estimated values of bootfun (the statistic for which CI's are computed). 
Lower, Upper 
numeric giving the estimated lower (upper) (1alpha)*100 percent CI's. 
plot.LocSig: invisibly returns a list containing the estimate as returned by LocSig, and the confidence range.
Eric Gilleland
Elmore, K. L., Baldwin, M. E. and Schultz, D. M. (2006) Field significance revisited: Spatial bias errors in forecasts as applied to the Eta model. Mon. Wea. Rev., 134, 519–531.
spatbiasFS
, tsboot
, boot
, boot.ci
, MCdof
, sig.cor.t
, sig.cor.Z
, cor.test
, image.plot
, as.image
1 2 3 4 5 6 7 8 9 10 11 12 13 14  ## Not run:
data( "GFSNAMfcstEx" )
data( "GFSNAMobsEx" )
data( "GFSNAMlocEx" )
id < GFSNAMlocEx[,"Lon"] >=90 & GFSNAMlocEx[,"Lon"] <= 75 & GFSNAMlocEx[,"Lat"] <= 40
look < LocSig(GFSNAMfcstEx[,id]  GFSNAMobsEx[,id], numrep=500)
stats(look)
plot(look, loc = GFSNAMlocEx[ id, ] )
## End(Not run)

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